Concluding Remarks

Spatial Data Programming with R

Bogdan G. Popescu
bogdan.popescu@johncabot.edu

John Cabot University

Learning Outcomes: Overview

Some of the learning outcomes in this course focused on:

  • executing basic programming tasks in R (e.g. loops, conditional statements, while statements, etc.)
  • understanding basic GIS (Geographic Information Systems) terms and concepts
  • utilizing GIS for conducting spatial analyses.
  • appreciating the design and structure of GIS as a decision-making tool.
  • producing maps

Skills Acquired

  • clean and process data
  • visualize data
  • create interactive web-apps
  • typeset: write visually appealing articles and presentations (R Quarto)

Jobs where these skills are valued

  • Data Scientist/Data Analyst

  • GIS Analyst/GIS Specialist

  • Environmental Scientist

  • Market Research Analyst

  • Remote Sensing Specialist

  • Transportation Planner

Use of R

R is also (more commonly) used in a variety of fields:

  • Finance
  • Academic research
  • Government
  • Retail
  • Data Journalism
  • Healthcare

Companies that use R

Examples of companies which use R include

  • Airbnb
  • Microsoft
  • Uber
  • Facebook
  • Google

Additional Good resources for learning R

  • R for Data Science
    http://r4ds.had.co.nz/
    Introduction to data analysis using R, focused on the tidyverse packages
    Good substitute for Stata

Good resources for learning R

Books to Use: Data Analysis and Visualization

Modern Statistics with R

R for Data Science

ggplot2

Books to Use: GIS

Geocomputation with R

Spatial Data Science

Other useful Sources

Overview of Processing Vector Layers

sf was the main library that we worked with

It helped us deal with:

  • Numerical Operations to calculate: Areas, Length, Distances, etc.
  • GIS Logical Operations: Overlaps, Equals, Intersects, etc.
  • Geometry Operations: Centroid, Buffer, Intersection, Union, Difference, etc.

Overview of Processing Raster Layers

We performed geometric operation on rasters (pictures) with the stars package:

  • Accessing cell values - as a matrix or as a dataframe, extracting pixels to points
  • Performing Raster algebra: raster arirthmentic and logic
  • Changing the resolution and extent: cropping, mosaicing, resampling, and reprojecting
  • Transforming Rasters: to points and polygons

Processing Raster Layers stars

Temperature in 1901

Processing Raster Layers stars

Temperature in 2022

Processing Raster Layers stars

Temperature difference between 2022 and 1901 > 4

Data visualization

  • ggplot2 is the library that allows to visualize data analysis results, but also to make maps
  • leaflet is a library that allows us to make interactive maps
  • mapview is a wrapper around leaflet automating the addition of: labels, popups, color scales, and common basemaps

Supplementary Lectures

I encourage you to check out the supplementary lectures:

Final Projects

This project offers an opportunity to showcase the acquired skills in manipulating spatial data and conducting meaningful analyses.

Tasks for the Project:

  • acquire data either from the course materials or from external sources
  • employ at least five GIS procedures in R such as: st_join, st_centroid, st_area, st_distance, st_buffer, st_voronoi, st_union, st_combine, st_cast, st_intersection, st_difference, dplyr for vector layer aggregation, st_crop, st_rasterize, raster::aggregate, etc.

Final Projects

Or

Produce some descriptive visualizations (maps, barplots, scatterplots, boxplots) that tell the same story AND make a Github website (for data, you can explore https://ourworldindata.org)

  • craft a well-structured two-page report (approx. 1500 words) containing an intro to the problem, objectives, data sources, methodology, results, and conclusion
  • well-documented appendix (with comments and hashtags) with the R code for the GIS procedures.

Grading Criteria for the project:

  • Relevance
  • Methodology: demonstration of at least five distinct GIS procedures in R (if applicable).
  • Analysis
  • Presentation
  • Code Quality

For presentation and memo length, please peruse the examples provided.

Instructions

Presentations will be on Friday, May 3rd in G.K.1.4, 09:00-11:30

Presentations should be 15 minutes.

Rehearse at least twice at home

Focus your presentation on the story

Criteria for Presentation Grading

  • Content Knowledge
  • Organization
  • Clarity of Expression
  • Engagement with Audience
  • Visual Aids
  • Time Management

Conclusion

Thank you and good luck!

Popescu (JCU): Concluding Remarks